This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a function with respect to parameters defining the distribution that is integrated; the problem of sensitivity analysis. In machine learning research, this gradient problem lies at the core of many learning problems, in supervised, unsupervised and reinforcement learning. We will generally seek to rewrite such gradients in a form that allows for Monte Carlo estimation, allowing them to be easily and efficiently used and analysed. We explore three strategies--the pathwise, score function, and measure-valued gradient estimators-- exploring their historical developments, derivation, and underlying assumptions. We describe their use in other fields, show how they are related and can be combined, and expand on their possible generalisations. Wherever Monte Carlo gradient estimators have been derived and deployed in the past, important advances have followed. A deeper and more widely-held understanding of this problem will lead to further advances, and it is these advances that we wish to support.

A futuristic 3D-printed house that lets guests'experience Mars on Earth' will soon offer you the chance to experience what an interplanetary vacation of the future may be like, its creators say. Nestled in the woods of upstate New York along the Hudson River, Tera will be hired out to holiday-makers hoping to experience what sustainable life could be like on Mars. 'Tera' is the brainchild of AI SpaceFactory, a New York City design agency that was awarded $500,000 (£386,000) earlier this year for winning NASA'S 3D-Printed Habitat Challenge with its previous'Marsha' habitat. Each stay will be used to fund the mission of the firm behind its design, which hopes to research and develop the renewable and sustainable technologies of the future. This technology will be used both here on Earth and, they say, will be one day form the basis of a sustainable colony on the red planet.

Attitudes about nuclear energy are changing, with pundits on both sides of the aisle touting its benefits for extremely efficient and relatively clean energy. Despite an ever more positive public opinion, the nuclear industry in the United States, the largest in the world, is currently experiencing a downturn, even going so far as to need government subsidies to keep afloat. In fact, at present the fastest growing sector of the nuclear industry is profiting not off of growth, but off of the nuclear sector's slow death in the United States. According to reporting by Bloomberg, "the fastest growing part of the nuclear industry in the U.S. involves a small but expanding group of companies that specialize in tearing reactors down faster and cheaper than ever before." Tearing down old nuclear reactors is no easy feat, however.

Slender marine structures such as deep-water marine risers are subjected to currents and will normally experience Vortex Induced Vibrations (VIV), which can cause fast accumulation of fatigue damage. The ocean current is often three-dimensional (3D), i.e., the direction and magnitude of the current vary throughout the water column. Today, semi-empirical tools are used by the industry to predict VIV induced fatigue on risers. The load model and hydrodynamic parameters in present VIV prediction tools are developed based on two-dimensional (2D) flow conditions, as it is challenging to consider the effect of 3D flow along the risers. Accordingly, the current profiles must be purposely made 2D during the design process, which leads to significant uncertainty in the prediction results. Further, due to the limitations in the laboratory, VIV model tests are mostly carried out under 2D flow conditions and thus little experimental data exist to document VIV response of riser subjected to varying directions of the current. However, a few experiments have been conducted with 3D current. We have used results from one of these experiments to investigate how well 1) traditional and 2) an alternative method based on a data driven prediction can describe VIV in 3D currents. Data driven modelling is particularly suited for complicated problems with many parameters and non-linear relationships. We have applied a data clustering algorithm to the experimental 3D flow data in order to identify measurable parameters that can influence responses. The riser responses are grouped based on their statistical characteristics, which relate to the direction of the flow. Furthermore we fit a random forest regression model to the measured VIV response and compare its performance with the predictions of existing VIV prediction tools (VIVANA-FD).

Probabilistic optimal power flow (POPF) is an important analytical tool to ensure the secure and economic operation of power systems. POPF needs to solve enormous nonlinear and nonconvex optimization problems. The huge computational burden has become the major bottleneck for the practical application. This paper presents a deep learning approach to solve the POPF problem efficiently and accurately. Taking advantage of the deep structure and reconstructive strategy of stacked denoising auto encoders (SDAE), a SDAE-based optimal power flow (OPF) is developed to extract the high-level nonlinear correlations between the system operating condition and the OPF solution. A training process is designed to learn the feature of POPF. The trained SDAE network can be utilized to conveniently calculate the OPF solution of random samples generated by Monte-Carlo simulation (MCS) without the need of optimization. A modified IEEE 118-bus power system is simulated to demonstrate the effectiveness of the proposed method.

This paper studies reinforcement learning (RL) under malicious falsification on cost signals and introduces a quantitative framework of attack models to understand the vulnerabilities of RL. Focusing on $Q$-learning, we show that $Q$-learning algorithms converge under stealthy attacks and bounded falsifications on cost signals. We characterize the relation between the falsified cost and the $Q$-factors as well as the policy learned by the learning agent which provides fundamental limits for feasible offensive and defensive moves. We propose a robust region in terms of the cost within which the adversary can never achieve the targeted policy. We provide conditions on the falsified cost which can mislead the agent to learn an adversary's favored policy. A numerical case study of water reservoir control is provided to show the potential hazards of RL in learning-based control systems and corroborate the results.

AI in Oil & Gas market is projected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66% from 2017 to 2022. Northbrook, IL -- (SBWIRE) -- 06/20/2019 -- AI in Oil & Gas market is expected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66%, during the forecast period. The growth of AI in Oil & Gas market will be mainly driven by the rise in adoption of the big data technology in the Oil & Gas industry to augment E&P capabilities, a significant increase in venture capital investments, and growing need for automation in the Oil & Gas industry, and tremendous pressure to reduce production costs. Software in AI in Oil & Gas market is applicable in upstream Oil & Gas exploration and production activities. The hardware segment in AI in Oil & Gas market is expected to grow swiftly during the forecast period (2017 to 2022), mainly due to the increasing requirement for sophisticated hardware system configurations and components capable of handling massive data, including, but not limited to Tensor Processor Unit (TPU), Graphic Processing Unit (GPU), Resistive Processing Unit (RPU), Field Programmable Gate Array (FPGA), and Visual Processing Unit (VPU) to install software-based AI capabilities.

AI in Oil & Gas market is projected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66% from 2017 to 2022. Northbrook, IL -- (SBWIRE) -- 06/20/2019 -- AI in Oil & Gas market is expected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66%, during the forecast period. The growth of AI in Oil & Gas market will be mainly driven by the rise in adoption of the big data technology in the Oil & Gas industry to augment E&P capabilities, a significant increase in venture capital investments, and growing need for automation in the Oil & Gas industry, and tremendous pressure to reduce production costs. Software in AI in Oil & Gas market is applicable in upstream Oil & Gas exploration and production activities. The hardware segment in AI in Oil & Gas market is expected to grow swiftly during the forecast period (2017 to 2022), mainly due to the increasing requirement for sophisticated hardware system configurations and components capable of handling massive data, including, but not limited to Tensor Processor Unit (TPU), Graphic Processing Unit (GPU), Resistive Processing Unit (RPU), Field Programmable Gate Array (FPGA), and Visual Processing Unit (VPU) to install software-based AI capabilities.

One of the big promises of the Internet of Things (IoT) is understanding the physical world around us and taking action based on insights and observations. Over the last decade, we've gotten really good at the first part, using smart devices and sensors for monitoring and data collection. We have sensors everywhere, in consumer products, on the floor and embedded in manufacturing and industry, distributed across nature and remote areas of the world--always on and always streaming new readings as they happen. This has transformed our understanding of how we work and live because we have more up-to-the-second data and analysis than ever before. The next area ripe for innovation is what we do with that data.

As climate change escalates, the impact of natural disasters is likely to become less predictable. To encourage the use of machine learning for building damage assessment this week, Carnegie Mellon University's Software Engineering Institute and CrowdAI -- the U.S. Department of Defense's Joint AI Center (JAIC) and Defense Innovation Unit -- open-sourced a labeled data set of some of the largest natural disasters in the past decade. Called xBD, it covers the impact of disasters around the globe, like the 2010 earthquake that hit Haiti. "Although large-scale disasters bring catastrophic damage, they are relatively infrequent, so the availability of relevant satellite imagery is low. Furthermore, building design differs depending on where a structure is located in the world. As a result, damage of the same severity can look different from place to place, and data must exist to reflect this phenomenon," reads a research paper detailing the creation of xBD.